template-agenticide-vibecoding

🥈Silver

Template for AI-first software development using Spec-Driven and Test-Driven approaches. Enables AI agent teams to replace traditional human development teams. Integrates with Git workflows and MCP servers for intelligent automation.

57120Updated 1mo ago

Intermediate30min to implementautomation

Saves ~360 min per use

Quick InstallView Source

git clone https://github.com/quboqin/template-agenticide-vibecoding.git

Works with:

Claude

Overview

About This Skill

This skill provides a complete customized development workflow template designed for Claude Code environments, supporting the full software development lifecycle from requirements analysis to system monitoring. It implements a multi-agent system with nine specialized roles—including product managers, architects, frontend/backend developers, DevOps, QA, and security experts—that collaborate through Spec-Driven and Test-Driven development methodologies. The template integrates Git Worktree for parallel development, MCP servers for tool integration, and automated CI/CD pipelines with quality gates. It solves the problem of coordinating AI-assisted development teams by providing predefined agent workflows, customizable commands, and task management systems. Organizations building complex software projects benefit from reduced development cycle times, improved code quality, and structured team coordination.

How to Use

Clone the template repository and copy the CLAUDE.md configuration to your Claude Code config directory. Initialize Git Worktree environments using provided scripts, then configure MCP servers (Context7, Sequential, Magic, Playwright). Start projects using Product Manager Agent commands like `/product-analyze` and `/project-init`, create Agent workspaces, and use specialized commands like `/implement`, `/test-gen`, and `/deploy-prep` for each role.

Use Cases

Multi-agent AI team coordination for large-scale software projects

Spec-Driven and Test-Driven development automation with Claude Code

Parallel development across frontend, backend, and DevOps teams using Git Worktree

Automated CI/CD pipelines with quality gates and monitoring integration

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/quboqin/template-agenticide-vibecoding

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Create a software development plan for [COMPANY] in the [INDUSTRY] sector using the template-agenticide-vibecoding framework. The project should focus on [SPECIFIC SOFTWARE GOAL], with a team of AI agents replacing traditional human developers. Include integration with Git workflows and MCP servers for intelligent automation. Provide a detailed timeline, resource allocation, and expected outcomes.

Example Output

# Software Development Plan for GreenTech Solutions

## Project Overview
- **Company**: GreenTech Solutions
- **Industry**: Renewable Energy
- **Goal**: Develop an AI-driven energy management system
- **Framework**: Template-agenticide-vibecoding
- **Team**: AI Agent Team Alpha

## Development Timeline
- **Phase 1: Requirements Gathering (Week 1-2)**
  - AI agents analyze market trends and user needs
  - Specifications are generated and reviewed
- **Phase 2: Design and Prototyping (Week 3-4)**
  - System architecture is designed
  - Prototypes are created and tested
- **Phase 3: Development (Week 5-12)**
  - AI agents write and test code
  - Continuous integration with Git workflows
- **Phase 4: Deployment (Week 13-14)**
  - System is deployed to MCP servers
  - Final testing and optimization

## Resource Allocation
- **AI Agents**: 5 (1 Lead, 4 Developers)
- **MCP Servers**: 3 (1 for development, 2 for testing)
- **Git Repositories**: 2 (1 for main branch, 1 for testing)

## Expected Outcomes
- A fully functional AI-driven energy management system
- Reduced development time by 40%
- Improved system reliability and performance